from IPython import display
display.Image('team.png', width=700)
# data: http://www.amber.utah.edu/AMBER-workshop/London-2015/DNA-tutorial/
import pytraj as pt
traj0 = pt.load('md.nc', 'dna.prmtop')
traj0
pytraj.Trajectory, 831 frames:
Size: 0.298563 (GB)
<Topology: 16074 atoms, 5144 residues, 5122 mols, PBC with box type = truncoct>
traj = traj0.autoimage()['!:WAT']
traj
pytraj.Trajectory, 831 frames:
Size: 0.014488 (GB)
<Topology: 780 atoms, 46 residues, 24 mols, PBC with box type = truncoct>
# compute rmsd and convert raw data to pandas' DataFrame
data = pt.rmsd(traj, ref=0, mask=':1-14&!@H=', dtype='dataframe')
data.head(5)
| RMSD_00001 | |
|---|---|
| 0 | 4.959681e-07 |
| 1 | 6.372364e-01 |
| 2 | 6.766345e-01 |
| 3 | 7.819916e-01 |
| 4 | 9.697053e-01 |
%matplotlib inline
data.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x2aaad72e5828>
data.hist()
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2aaad93a4f98>]], dtype=object)
traj = pt.iterload('md.nc', 'dna.prmtop')
data = pt.pmap(pt.rmsd, traj, ref=0, mask=':1-14&!@H=', n_cores=8)
# serial: data = pt.rmsd(traj, ref=0, mask=':1-14&!@H=')
from IPython import display
display.Image('bench_pmap_casegroup.png', width=500)
traj2 = pt.iterload('tz2.nc', 'tz2.parm7')
energies = pt.energy_decomposition(traj2, igb=8, dtype='dataframe')
energies[['bond', 'angle', 'dihedral', 'gb']].head()
| bond | angle | dihedral | gb | |
|---|---|---|---|---|
| 0 | 0.015314 | 128.545148 | 111.611329 | -412.532664 |
| 1 | 0.013582 | 105.064945 | 105.392413 | -400.090422 |
| 2 | 0.012521 | 103.520284 | 93.030850 | -439.927013 |
| 3 | 0.016334 | 94.560780 | 105.522288 | -400.956276 |
| 4 | 0.013338 | 99.508124 | 105.850222 | -404.061030 |
# XTC
import mdtraj as md
t0 = md.load('./monolayer.xtc', top='monolayer.pdb')
coordinates = t0.xyz.astype('f8')
traj = pt.Trajectory(xyz=coordinates, top='monolayer.pdb')
pt.center_of_mass(traj)
array([[ 2.93015586, 2.60094379, 1.66991936],
[ 3.01727943, 2.57976876, 1.59680848],
[ 3.02472708, 2.57837296, 1.58989385],
...,
[ 3.0366626 , 2.57732852, 1.5900617 ],
[ 3.08111659, 2.58228309, 1.59082286],
[ 3.08029504, 2.58351248, 1.58572677]])